Source: Cointelegraph
Original: “AI Tool Claims 97% Efficacy in Preventing Address Poisoning Attacks”
Cryptocurrency cybersecurity company Trugard has developed an AI-based system in collaboration with the on-chain trust protocol Webacy to detect cryptocurrency wallet address poisoning attacks.
According to an announcement shared with Cointelegraph on May 21, this new tool is part of Webacy's crypto decision-making tools, “utilizing supervised machine learning models trained on real-time transaction data, on-chain analysis, feature engineering, and behavioral context.”
It is claimed that this new tool has a success rate of up to 97% in testing against known attack cases. Webacy co-founder Maika Isogawa stated, “Address poisoning is one of the underreported but highly damaging scams in the cryptocurrency space, exploiting the simplest assumption: what you see is what you get.”
Cryptocurrency address poisoning is a scam where attackers send a small amount of cryptocurrency from a wallet address that is very similar to the target's real address, often with the same starting and ending characters. The goal is to trick users into inadvertently copying and using the attacker's address in future transactions, leading to financial loss.
This technique exploits users' tendency to rely on partial address matching or clipboard history when sending cryptocurrency. A study conducted in January 2025 found that between July 1, 2022, and June 30, 2024, there were over 270 million address poisoning attempts on the BNB Chain and Ethereum. Among these, 6,000 attempts were successful, resulting in losses exceeding $83 million.
Trugard's Chief Technology Officer Jeremiah O’Connor told Cointelegraph that their team brought deep cybersecurity expertise from the Web2 world and “applied it to Web3 data since the early days of cryptocurrency.” The team applied their experience in algorithm feature engineering from traditional systems to Web3. He added:
“Most existing Web3 attack detection systems rely on static rules or basic transaction filtering. These methods often fail to keep up with the evolving strategies, techniques, and procedures of attackers.”
The newly developed system utilizes machine learning to create a system capable of learning and adapting to address poisoning attacks. O’Connor emphasized that the uniqueness of their system lies in its “focus on context and pattern recognition.” Isogawa explained, “AI can detect patterns that often exceed human analytical capabilities.”
O’Connor stated that Trugard generated synthetic training data for the AI to simulate various attack patterns. The model was then trained through supervised learning, a type of machine learning that involves training the model on labeled data, including input variables and correct outputs.
In this setup, the goal is for the model to learn the relationship between inputs and outputs to predict the correct output for new, unseen inputs. Common examples include spam detection, image classification, and price prediction.
O’Connor mentioned that as new strategies emerge, the model will also be updated through training on new data. He said, “Most importantly, we built a synthetic data generation layer that allows us to continuously test the model's performance against simulated poisoning scenarios. This is very effective in helping the model generalize and maintain robustness over the long term.”
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